This paper addresses the challenge of enhancing medical imaging analysis on edge devices while maintaining patient privacy and security. In this paper, we present a novel federated autoencoder model, U-NeTrans, which prioritizes security and privacy and is designed for medical image reconstruction on edge devices. U-NeTrans uses random masking to increase training complexity while maintaining manageability by using partial data. Data secrecy is ensured by the encoder processing visible patches and the decoder using encoded data to reassemble the original image. U-NeTrans improves the representation of high-order features in medical images by combining auxiliary reconstruction tasks and contrastive loss. This allows for precise analysis while maintaining patient privacy. The proposed method has wide ramifications for chest X-ray analysis and other medical imaging applications and offers the potential to improve healthcare device capabilities at the edge significantly. Comparative experimental results with benchmark datasets highlight the effectiveness of U-NeTrans compared to state-of-the-art approaches for edge-based medical image analysis while maintaining security and privacy. Accuracy, precision, sensitivity, specificity, and AUROC are measured across multiple scales and are shown to total 98.97%, 98.68%, 98.73%, and 99.19%, respectively.

Breast cancer is the most prevalent cancer among women and poses a significant global health challenge due to its association with uncontrolled cell proliferation. Artificial intelligence (AI) integration into medical practice has shown promise in boosting diagnosis accuracy and treatment protocol optimisation, thus contributing to improved survival rates globally. This paper presents a comprehensive analysis utilizing the Wisconsin Breast Cancer dataset, comprising data from 569 patients and 30 attributes. We propose BreastCancerNet, a hybrid AI architecture that leverages dual deep neural networks (DNNs) coupled with an attention mechanism to enhance breast cancer diagnosis. The proposed framework integrates two distinct DNNs (DNN-I and DNN-II) to extract diverse feature representations from the dataset, which are then concatenated for comprehensive analysis. An attention mechanism is employed to prioritize critical features, thereby improving the model's focus on essential characteristics of the input data. The final classification is performed using a support vector machine (SVM), achieving an impressive accuracy rate of 99.42% in differentiating between malignant and benign cases. Furthermore, we introduce a user-centric web application that facilitates real-time breast cancer detection by allowing users to input new attributes. This intuitive web interface fosters interactive engagement with the predictive algorithm, potentially enhancing breast cancer screening and treatment outcomes.

Efficient Medium Access Control (MAC) protocols are crucial for time-sensitive transmissions of safety and non-safety messages. The IEEE 802.11p standard requires enhancements for varying channel conditions, including error-prone environments. This paper proposes HVMAC, a hybrid VANET MAC protocol that enhances Quality of Service (QoS) for time-sensitive data traffic applications. It categorizes service channels into contention and scheduled channels. The HVMAC protocol is modeled with Markov chains and evaluated against IEEE 802.11p on offered load, average delay, throughput, reliability, and energy consumption (upto 90%). To improve HVMAC security during safety messages (SM) and service advertisement messages (SAM) transmission, a Post-Quantum Weighted Anonymous Authentication (PQWAA) is proposed. The weighted certificate authority (WCA) assigns priority weights to vehicles, for effective traffic management and resource distribution. PQWAA ensures secure authentication and message integrity using quantum-resistant cryptography and pseudonym-based key derivation. Integrating PQWAA with HVMAC ensures energy efficiency and secure communication for both safety and non-safety applications, offering a comprehensive solution for modern VANET environments. The proposed protocol is analyzed with existing techniques based on the packet delivery ratio, throughput, and average packet delay, values of 99%, (45-50)Mbps, and 50 ms. Also, the HVMAC protocol shows 81.23% lower channel collision than TDMA-MAC.

The automotive industry is transforming from traditional private vehicle ownership to innovative shared mobility solutions, presenting unprecedented cybersecurity challenges. This transition introduces complex security vulnerabilities where malicious actors could exploit the access of a rental vehicle to manipulate the software systems on board. Unlike physical damage, which can be easily detected, software modifications represent an insidious threat that can compromise user safety and vehicle integrity. Our research proposes a blockchain-based approach to address these critical security challenges. We introduce a novel method for ensuring data authenticity and integrity within vehicle systems by leveraging blockchain’s immutable ledger and advanced encryption technologies. Our methodology utilizes the Trusted Platform Module (TPM) to securely archive vehicle data in the central gateway, creating a tamper-evident environment that fundamentally transforms traditional data management approaches. The key innovation lies in the blockchain-based data binding process: when a user possesses a vehicle, they bind application-retrieved data with the vehicle’s existing data and commit them to the blockchain. Upon vehicle return, any potential tampering can be immediately detected by comparing newly acquired data against pre-existing blockchain records. We develop a proof-of-concept implementation and demonstrate significant improvements in security architecture that offer a reliable alternative to conventional database-centric approaches. Comparative evaluations between database-centric and blockchain-centric architectures testify to the operational effectiveness and practical viability of our proposed solution. By addressing the inherent vulnerabilities in shared mobility ecosystems, this research contributes to a sophisticated technological intervention that enhances user safety, data integrity, and trust in emerging transportation paradigms.

The global maritime industry is undergoing a digital transformation driven by the Fourth Industrial Revolution, which gives rise to “smart ports” or “Port 4.0” environments. These ports leverage smart technologies, and thus increase exposure to cyber threats that have seen dramatic growth in recent years. This paper presents four key contributions to strengthen cybersecurity in smart Port ecosystems. First, it maps the current cyber-threat landscape across both Information Technology (IT) and Operational Technology (OT) systems used in the smart Port environment, highlighting critical vulnerabilities. Then, it analyzes existing regulatory and standard frameworks such as the International Maritime Organization (IMO) guidelines, ISO/IEC 27001, and the National Institute of Standards and Technology (NIST) cybersecurity framework, identifying alignment gaps with maritime operational realities. Further, this paper also provides structured, Port-specific cybersecurity recommendations tailored to the complex interplay of legacy OT systems and modern digital technologies. Finally, the paper discusses AI-assisted cybersecurity solutions available in the literature, highlighting how advanced AI-based analytics, predictive modeling, and automated incident response can be incorporated. The insights presented are intended to help Port authorities build resilient, adaptive cybersecurity postures in an increasingly interconnected maritime domain.

The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time patient monitoring, remote diagnostics, and intelligent decision-making. However, IoMT data is prone to unauthorized access, resulting in a significant loss of privacy and security. To address these challenges, we propose a novel security framework which is based on XChaCha20-Encryption fortified with a Role-Based Access Control (RBAC) mechanism and a blockchain-integrated ML model to establish a highly secure and intelligent healthcare ecosystem. The proposed framework employs a Proof of Authority (PoA) consensus mechanism to validate and secure blockchain operations, making it particularly well-suited for real time IoMT applications. The performance of the system is evaluated on the WUSTL-EHMS-2020 dataset, demonstrating superior results over other state-of-the-art approaches. Moreover, the proposed framework achieves remarkable encryption and decryption times of 0.61 and 0.71 seconds, respectively. Along with data privacy proposed framework outperforms other methods and achieves an accuracy of 99.43% for detecting cyber attacks launched against IoMT data.

ABSTRACT Methodology Human activity recognition (HAR) has emerged as a fundamental capability in various disciplines, including ambient assisted living, healthcare, human‐computer interaction, etc. This study proposes a novel approach for activity recognition by integrating IoT technologies with Artificial Intelligence and Edge Computing. This work presents a fusion HAR approach that combines data readings from wearable sensors such as accelerometer and gyroscope sensors and Images captured by vision‐based sensors such as cameras incorporating the capabilities of Long Short‐Term Memory (LSTM) and Convolutional Neural Network (CNN) models. The aim of fusing these models is to capture and extract the temporal and spatial information, improving the accuracy and resilience of activity identification systems. The work uses the CNN model to find spatial features from the images that represent the contextual information of the activities and the LSTM model for processing sequential accelerometer and gyroscope sensor data to extract the temporal dynamics from the human activities. Results The performance of our fusion approach is evaluated through different experiments using varying parameters and applies the best‐suited parameters for our model. The results demonstrate that the fusion of LSTM and CNN models outperforms standalone models and traditional fusion methods, achieving an accuracy of 98%, which is almost 9% higher than standalone models. Conclusion The fusion of LSTM and CNN models enables the integration of complementary information from both data sources, leading to improved performance. The computation tasks are performed at the local edge device resulting to enhanced privacy and reduced latency. Our approach greatly impacts real‐world applications where accurate and reliable HAR systems are essential for enhancing human‐machine interaction and monitoring human activities in various domains.

Wireless sensor networks (WSNs) can be conceptualized as large-scale, dynamic social systems where nodes interact to achieve collective objectives. These networks generate extensive data through interactions, offering opportunities for large-scale knowledge discovery to optimize operations and enhance resilience. However, challenges such as limited resources and susceptibility to distributed denial-of-service (DDoS) attacks necessitate efficient and secure mechanisms for managing these “social” interactions. This article proposes a lightweight trusted framework that applies computational modeling principles to clustering in WSNs. The framework employs bi-directional long short-term memory (Bi-LSTM) networks for malicious node detection, mirroring the role of anomaly detection in social systems, and uses the walrus optimization algorithm (WOA) for optimized cluster head (CH) selection. By considering parameters such as residual energy, proximity to base stations, node density, and trust value, WOA ensures effective “role assignment” within the network, similar to optimizing functional roles in human social systems. The Bi-LSTM model analyzes node behavior to exclude malicious actors, fostering trusted, and efficient clustering. Evaluated in simulated DDoS attack scenarios, the framework significantly reduces the impact of attacks by isolating malicious nodes while improving network performance and resilience. Metrics such as stability period, throughput, network lifetime, energy efficiency, and attack mitigation are analyzed, demonstrating the framework’s effectiveness. This research bridges the domains of social system modeling and WSN operations, providing an energy-efficient and secure solution for managing large-scale dynamic networks.

The integration of the Internet of Drone Things (IoDT) with spatial crowdsourcing, enhanced by 6G technology, has revolutionized environmental monitoring, particularly in managing Australian bushfires. This approach leverages drones’ mobility, multidimensional motion, and ease of deployment to gather real-time data from hazardous or inaccessible areas. However, the unsecured wireless communication channels and limited computational resources of drones in typical IoDT scenarios make them susceptible to cyber-attacks, including spoofing, GPS manipulation, impersonation, man-in-the-middle, and hijacking. To counter these threats, we propose a robust security protocol that utilizes blockchain technology augmented by Hyperelliptic Curve Cryptography (HECC). By employing blockchain as a Certificate Authority (CA) and treating transactions as certifications, our framework, DronCert, eliminates the need for traditional CAs or Trusted Third Parties (TTP). This decentralized approach, combined with the high-speed, low-latency capabilities of 6G, significantly enhances data transmission security within the IoDT network. A comprehensive security analysis demonstrates DronCert’s resilience against various attacks, such as Denial-of-Service (DoS), man-in-the-middle, replay, and unauthorized device representation.

In this research, we introduce two new machine learning regression methods: the Ensemble Average and the Pipelined Model. These methods aim to enhance traditional regression analysis for predictive tasks and have undergone thorough evaluation across three datasets, Kaggle House Price, Boston House Price, and California Housing, using various performance metrics. The results consistently show that our models outperform existing methods in terms of accuracy and reliability across all three datasets. The Pipelined Model, in particular, is notable for its ability to combine predictions from multiple models, leading to higher accuracy and impressive scalability. This scalability allows for their application in diverse fields like technology, finance, and healthcare. Furthermore, these models can be adapted for real-time and streaming data analysis, making them valuable for applications such as fraud detection, stock market prediction, and IoT sensor data analysis. Enhancements to the models also make them suitable for big data applications, ensuring their relevance for large datasets and distributed computing environments. It is important to acknowledge some limitations of our models, including potential data biases, specific assumptions, increased complexity, and challenges related to interpretability when using them in practical scenarios. Nevertheless, these innovations advance predictive modeling, and our comprehensive evaluation underscores their potential to provide increased accuracy and reliability across a wide range of applications. The results indicate that the proposed models outperform existing models in terms of accuracy and robustness for all three datasets. The source code can be found at https://huggingface.co/DebajyotyBanik/Ensemble-Pipelined-Regression/tree/main

Internet of Health Things (IoHT) is a promising e-Health paradigm that involves offloading numerous computational-intensive and delay-sensitive tasks from locally limited IoHT points to edge servers (ESs) with abundant computational resources in close proximity. However, existing computation offloading techniques struggle to meet the burgeoning health demands in ultra-reliable and low-latency communication (URLLC), one of the 5G application scenarios. This article proposes a Multi-Agent Soft-Actor-Critic-discrete based URLLC-constrained task offloading and resource allocation (MASACDUA) scheme to maximize throughput while minimizing power consumption on the remote side, considering the long-term URLLC constraints. The URLLC constraint conditions are formulated using extreme value theory, and Lyapunov optimization is employed to divide the problem into task offloading and computation resource allocation. MASAC-discrete and a queue backlog-aware algorithm are utilized to approach task offloading and computation resource allocation, respectively. Extensive simulation results demonstrate that MASACDUA outperforms traditional DRL algorithms under different IoHT points and data arrival rate intervals and achieves superior performance in delay, bound violation probability, and other characteristics related to URLLC.

The Internet of Vehicles (IoV) is a network of vehicles that are connected through the Internet using communication protocols. However, this raises concerns about security and privacy. The vulnerability of object networks has been highlighted in recent reports on cybernetics, and attacks on these networks could pose significant dangers to data integrity. To avoid this, security measures such as resistance to attacks, data authenticity, access control, and privacy must be integrated. The limited capabilities of IoT objects in terms of processing power, memory, bandwidth, and energy have led to the creation and redesign of protocols and architectures that meet the specific needs of these networks. One such protocol is the Message Queue Telemetry Transport (MQTT), which uses a Publish/Subscribe methodology and small amounts of bandwidth. To address security concerns, the paper proposes using the lightweight cryptographic techniques ASCON and GIFT-COFB, which could be useful in securing communication and data exchange in IoV sensor networks.

Lung cancer has been the leading cause of cancer death for many decades. With the advent of artificial intelligence, various machine learning models have been proposed for lung cancer detection (LCD). Typically, challenges in building an accurate LCD model are the small‐scale datasets, the poor generalizability to detect unseen data, and the selection of useful source domains and prioritization of multiple source domains for transfer learning. In this paper, a multiround transfer learning and modified generative adversarial network (MTL‐MGAN) algorithm is proposed for LCD. The MTL transfers the knowledge between the prioritized source domains and target domain to get rid of exhaust search of datasets prioritization among multiple datasets, maximizing the transferability with a multiround transfer learning process, and avoiding negative transfer via customization of loss functions in the aspects of domain, instance, and feature. In regard to the MGAN, it not only generates additional training data but also creates intermediate domains to bridge the gap between the source domains and target domains. 10 benchmark datasets are chosen for the performance evaluation and analysis of the MTL‐MGAN. The proposed algorithm has significantly improved the accuracy compared with related works. To examine the contributions of the individual components of the MTL‐MGAN, ablation studies are conducted to confirm the effectiveness of the prioritization algorithm, the MTL, the negative transfer avoidance via loss functions, and the MGAN. The research implications are to confirm the feasibility of multiround transfer learning to enhance the optimal solution of the target model and to provide a generic approach to bridge the gap between the source domain and target domain using MGAN.

The combination of nonorthogonal multiplex access and unmanned aerial vehicles (UAVs) can improve the energy efficiency (EE) for Internet of Things (IoT). On the condition of interference constraint and minimum achievable rate of the secondary users, we propose an iterative optimization algorithm on EE. First, with a given UAV trajectory, the Dinkelbach method-based fractional programming is adopted to obtain the optimal transmission power factors. By using the previous power allocation scheme, the successive convex optimization algorithm is adopted in the second stage to update the system parameters. Finally, reinforcement-learning-based optimization is introduced to obtain the best UAV trajectory.

The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted much attention and need to be solved urgently. In the teledermatology healthcare framework, the smartphone can acquire dermatology medical images for remote diagnosis. The dermatology medical image is vulnerable to attacks during transmission, resulting in malicious tampering or privacy data disclosure. Therefore, there is an urgent need for a watermarking scheme that doesn't tamper with the dermatology medical image and doesn't disclose the dermatology healthcare data. Federated learning is a distributed machine learning framework with privacy protection and secure encryption technology. Therefore, this paper presents a robust zero-watermarking scheme based on federated learning to solve the privacy and security issues of the teledermatology healthcare framework. This scheme trains the sparse autoencoder network by federated learning. The trained sparse autoencoder network is applied to extract image features from the dermatology medical image. Image features are undergone to two-dimensional Discrete Cosine Transform (2D-DCT) in order to select low-frequency transform coefficients for creating zero-watermarking. Experimental results show that the proposed scheme has more robustness to the conventional attack and geometric attack and achieves superior performance when compared with other zero-watermarking schemes. The proposed scheme is suitable for the specific requirements of medical images, which neither changes the important information contained in medical images nor divulges privacy data.

Nowadays, developing environmental solutions to ensure the preservation and sustainability of natural resources is one of the core research topics for providing a better life quality. Using renewable energy sources, such as solar energy, is one of the solutions that can reduce the overuse of natural resources. This research aims to boost the efficiency of solar energy plants by proposing a novel approach to optimize the total flying time of battery-based drone systems to enhance the performance of solar plant systems. The contribution of the proposed approach is to solve scheduling problems based on timing constraints to monitor the solar plant. The main objective of the proposed approach is to maximize the drone’s minimum total flying time, which will increase the availability and reliability of the solar plant monitoring system. Time to empty values is calculated based on battery degradation rates. This problem is proven to be NP-hard. Four categories of enhanced algorithms were developed to solve drones’ scheduling problems in handling various tasks within multiple errands in the extent of solar parks in the monitored power plant to achieve the desired objective. Experimental results of the presented algorithms showed that the$M2S$algorithm has a stable performance behavior in all conducted experiments.

How to efficiently process concurrent data tasks such as online analytical queries in datacenter environments is still a big challenge for current computing techniques. One of the fundamental reasons is that their task execution normally involves large numbers of distributed data operators, which are always expensive in terms of communication time. To improve the general performance, various advanced approaches on the execution optimization of data operators have been proposed in the past years. However, most of them focus on application-level optimization, such as using data locality scheduling to reduce network traffic. Moreover, few of them has considered the optimization opportunities for concurrent execution of multiple data operators. In this paper, we propose a novel coflow-based scheduling system called CoFlop, which aims to improve network communication time for multiple distributed operators at a query level, and on that basis to lay a solid foundation for the development of a network-aware query execution system in datacenter networks. We introduce the detailed system design of CoFlop and conduct a simulation-based evaluation with large concurrent distributed join operations. Compared to existing methods, the experimental results show that CoFlop can perform better in the presence of different large workloads.

In the process of drone development, most of the current state systems’ design is based on high-weight functionalities. Due to high-weight functionalities, it is observed that if the drone drops at a particular point, the entire design is fragmented. Also, well-defined functionalities of drones for a specific application can only be designed if radial functionalities are defined at proper angles. Therefore, this article addresses the issues present in the existing method using the CRA algorithm, where radial functions, represented in terms of input and hidden weighting functions, are explored utterly. Additionally, a novel analytical procedure that establishes the coverage area for the data transfer approach has been incorporated into the drones’ architecture. Additionally, employing motion signatures and a special identification system, the developed drone system can function along various paths. To evaluate the effectiveness of the suggested system, three scenarios are organized as a basic functionality model. With the right scattering ratio, the comparison inscriptions show that the proposed approach can achieve an 82% success rate.

Elderly people are the assets of the country and the government can ensure their peaceful and healthier life. Life expectancy of individuals has expanded with technological advancements and survey tells that the elderly population will become double in the year 2030. The noninfectious cognitive dysfunction is the most important risk factor among elderly people due to a decline in their physiological function. Alzheimer, Vascular Dementia, and Dementia are the key reasons for cognitive inabilities. These diseases require manual assistance, which is difficult to provide in this fast-growing world. Prevention and early detection are the wise solution for the above diseases. Diabetes and hypertension are considered as main risk factors allied with Alzheimer's disease. Our proposed work applies a two-stage classification technique to improve prediction accuracy. In the first stage, we train a Support vector machine and a Random Forest algorithm to analyze the influence of diabetes and high blood pressure on cognitive decline. In the second stage, the cognitive function of the person with the possibility of Dementia is assessed using the neuropsychological test called Cognitive Ability Test (CAT). Multinomial Logistic Regression algorithm is applied to CAT results to predict the possibility of cognitive decline in their postlife. We classified the risk factor using the operational definitions: “No Alzheimer’s,” “Uncertain Alzheimer’s,” and “Definite Alzheimer’s”. SVM of stage 1 classifier predicts with an accuracy of 0.86 and Random Forest with an accuracy of 0.71. Multinomial Logistic algorithm of stage 2 classifier accuracy is 0.89. The proposed work enables early prediction of a person at risk of Alzheimer's Disease using clinical data.

The breakdown of vital communication infrastructures is one of the most common characteristics of all disasters. It can cause severe communication problems such as time delays and data loss, which cause deterioration in system performance. New techniques are needed to cope with such situations, many of which have been made possible due to the ongoing evolution of artificial intelligence technologies. In this study, we consider the case of a network consisting of several router allocation problems in situations of high priority and emergency data allocation. A novel network component called the scheduler is introduced and window constraints for routers are imposed. To solve the studied problem, four different algorithms are developed in this work. These algorithms were then applied in a particular scenario consisting of several routers and 2200 instances. In terms of the gap and running time, the proposed algorithms provide acceptable results. The best performances were achieved using the critical packet algorithm for 80% of instances with an average gap value of 0.009 and an average time of 0.209 s.

The integration of artificial intelligence and Internet of Things (IoT) has promoted the rapid development of artificial IoT (AIoT) networks. A wide range of AIoT applications have generated a great deal of data. The fifth-generation (5G) mobile communication has powerful data processing capabilities, and it is a key technology to enable AIoT big data processing. The explosive growth of the 5G users has made information security in AIoT networks a significant issue. Real-time security evaluation in AIoT networks is difficult due to user mobility and dynamic wireless environments. Thus, the evaluation and prediction of secrecy performance is a very critical research. In this article, new expressions for the nonzero secrecy capacity probability (NSCP) are derived to evaluate the mobile collaborative secrecy performance. An improved convolutional neural network (CNN) model, named as SI-CNN in this article, is proposed to predict the NSCP performance. The SI-CNN model combines the SqueezeNet and InceptionNet, and it has four convolution layers, which all adopt the same convolution model. For the first two layers, they employ a 2 × 1 convolution and a three-branch convolution, which not only increase the number of channels but also extract more features. For the last two layers, they employ the same structure, but different convolution kernels. The proposed SI-CNN prediction algorithm is shown to provide better NSCP performance prediction than other state-of-the-art methods. In particular, compared with wavelet neural network, the prediction precision of SI-CNN is improved by 26.8%.

In today’s world, one of the most severe attacks that wireless sensor networks (WSNs) face is a Black-Hole (BH) attack which is a type of Denial of Service (DoS) attack. This attack blocks data and injects infected programs into a set of sensors in a group to capture packets before reached to the target. Therefore, raw data in the BH region is thwarted and is unable to reach its destination. The network is susceptible to various types of attacks as it is accessible to all types of users and minimizing the energy depletion without compromising the network lifetime is an NP-hard problem. Even though numerous protocols came into effect to overcome the BH attack and to enhance the security of packet delivery in WSNs, Simulated Annealing Black-hole attack Detection (SABD) based Enhanced Gravitational Search Algorithm (EGSA) is yet another implemented strategy to reduce the BH attacks. EGSA-SABD detects and isolates the BH infectors in WSNs. Initially, sensor nodes are hierarchically clustered using similar residual energy to reduce energy consumption. Then, the BH attack possibility in a deployed node is evaluated to find the existence of BH nodes in the region. In the end, EGSA-SABD is employed to detect and quarantine BH attackers in WSNs. The performance of EGSA-SABD is evaluated with certain metrics such as BH attack detection probability rate (BHatt_Prate), energy consumption (Ec), Duration of BH attack detection (Attduration), Packet delivery ratio (Pdr). Based on the experimental observations, the EGSA-SABD outperforms the BHatt_Prate by 13% and also reduces the energy consumption by 21%.

Now a day’s advancement in technology increases the use of automation, mobility, smart devices, and application over the Internet that can create serious problems for protection and the privacy of digital data and raised the global security issues. Therefore, the necessity of intelligent systems or techniques can prevent and protect the data over the network. Cyberattack is the most prominent problem of cybersecurity and now a challenging area of research for scientists and researchers. These attacks may destroy data, system, and resources and sometimes may damage the whole network. Previously numerous traditional techniques were used for the detection and mitigation of cyberattack, but the techniques are not efficient for new attacks. Today’s machine learning and metaheuristic techniques are popularly applied in different areas to achieve efficient computation and fast processing of complex data of the network. This paper is discussing the improvements and enhancement of security models, frameworks for the detection of cyberattacks, and prevention by using different machine learning and optimization techniques in the domain of cybersecurity. This paper is focused on the literature of different metaheuristic algorithms for optimal feature selection and machine learning techniques for the classification of attacks, and some of the prominent algorithms such as GA, evolutionary, PSO, machine learning, and others are discussed in detail. This study provides descriptions and tutorials that can be referred from various literature citations, references, or latest research papers. The techniques discussed are efficiently applied with high performance for detection, mitigation, and identification of cyberattacks and provide a security mechanism over the network. Hence, this survey presents the description of various existing intelligent techniques, attack datasets, different observations, and comparative studies in detail.

Recent technological advances have fostered the development of complex industrial cyber-physical systems with communication delay requirements. The consequences of delay requirement violation in such systems may become increasingly severe. In this paper, we propose a contract-based fault-resilient methodology which aims at managing the communication delays of network flows in industries. With this objective, we present a lightweight mechanism to estimate end-to-end communication delays in the network where the clocks of the switches are not synchronized. The mechanism aims at providing high level of accuracy with little communication overhead. We then propose a contract-based framework using software-defined networking (SDN) where the components are associated with delay contracts and a resilience manager. The proposed resilience management framework contains: (1) contracts which state requirements about components' behaviors, (2) observers which are responsible to detect contract failure (fault), (3) monitors to detect events such as run-time changes in the delay requirements and link failure, (4) control logic to take suitable decisions based on the type of the fault, (5) resilience manager to decide response strategies containing the best course of action as per the control logic decision. Finally, we present a delay-aware path finding algorithm which is used to route/reroute the network flows to provide resilience in the case of faults and, to adapt to the changes in the network state. Performance of the proposed framework is evaluated with the Ryu SDN controller and Mininet network emulator.

The characteristics of MANET such as decentralized architecture, dynamic topologies make MANETs susceptible to various security attacks. Sequence number attacks are such type of security threats which tend to degrade the network functioning and performance by sending fabricated route reply packets (RREP) with the objective of getting involved in the route and drop some or all of the data packets during the data transmission phase. The sequence number adversary attempts to send a fabricated high destination number in the RREP packet which attracts the sender to establish a path through the adversary node. This paper proposes a proactive secure routing mechanism which is an improvement over the authors previously proposed scheme. It makes use of linear regression mechanism to predict the maximum destination sequence number that the neighboring node can insert in the RREP packet. As an additional security checkpoint, it uses a bait detection mechanism to establish confidence in marking a suspicious node as a malicious node. The proposed approach works in collaboration with the ad hoc on-demand distance vector routing (AODV) protocol. The simulation results depict that the approach improves the network performance in the presence of adversaries as compared to previously proposed scheme.

While Delay Tolerant Networks (DTNs) have exposed to offer guaranteeing additions to infrastructure based networks, they introduce new issues. As the nodes in the DTNs use store-carry-forward mechanism for routing, they need energy, battery power, bandwidth, memory or CPU utilization. Due to lack of these resources the node drops the packets. In this paper, firstly, we discuss different issues and research works carried out for selfish nodes' detection. Defending these selfish nodes accurately becomes vital in DTNs. In this paper, we present Cooperation Based Defense Mechanism (CBDM) against selfish nodes in DTNs. Furthermore, we study basic operations and security issues of Probabilistic Routing Protocol based on History Encounters and Transitivity (PRoPHET). This paper address a static mandatory cooperation scheme in which the nodes are forced to behave well. Moreover, a mechanism is used to calculate cooperation of selfishness of nodes by checking the number of sent messages and number of received messages. A bait detection scheme is used to confirm malicious behavior of suspicious nodes. Simulation results show that the proposed approach reduces the number of selfish nodes along with the decrease in the number of dropped packets and overhead ratio.